{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f7eb31b5-0a7e-465f-8156-b9a14935b56c",
   "metadata": {},
   "source": [
    "## Naive Baye's Machine Learning Algorithms Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f942f499-8e3b-4a99-a9ea-f432fedf51ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db67dcc9-a0bc-4443-a16e-1a3e4a585aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "241d0b77-319c-45a4-8259-542d77bae16b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=load_iris(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d2710f0-4a01-49d4-a7ba-03e2bcf9e5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbb22228-5deb-4c9e-8602-c09859159d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ec2f6ee-c147-49e2-a5f5-3fe17b2c8de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5. , 2. , 3.5, 1. ],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5.9, 3. , 5.1, 1.8],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [4.6, 3.2, 1.4, 0.2]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef1fde3c-2dd3-42d1-b2d3-40211cea5aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "60a2da00-7c50-420a-a978-e4b0cb618d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce278a97-43e8-4bf8-a541-d70f09e56af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd6d2d1f-a596-4cb4-a051-3b9bb54e5898",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f715a301-ec5c-4264-b217-e96f2b285707",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e747a2a1-0e79-4f68-a1e6-55392ed80b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16  0  0]\n",
      " [ 0 18  0]\n",
      " [ 0  0 11]]\n",
      "1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      1.00      1.00        18\n",
      "           2       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_pred,y_test))\n",
    "print(accuracy_score(y_pred,y_test))\n",
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "905c3702-3283-4823-a78e-39bfc54d1725",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b08f934-d66a-436e-ae7f-2a094ec36a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4422049-e066-4160-85d1-dac26bba81fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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